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Featured in Development

Peter Alvaro talks about the reasons one should engage in language design and why many of us would (or should) do something so perverse as to design a language that no one will ever use. He shares some of the extreme and sometimes obnoxious opinions that guided his design process.

Featured in AI, ML & Data Engineering

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynote speakers at QCon.ai.

Featured in Culture & Methods

Organizations struggle to scale their agility. While every organization is different, common patterns explain the major challenges that most organizations face: organizational design, trying to copy others, “one-size-fits-all” scaling, scaling in siloes, and neglecting engineering practices. This article explains why, what to do about it, and how the three leading scaling frameworks compare.

Using Deep Learning Technologies IBM Reaches a New Milestone in Speech Recognition

The research team at IBM recently announced they've reached a new industry record in speech recognition with a word error rate of 5.5% using the SWITCHBOARD linguistic corpus. This brings it closer to what's considered to be the human error rate of 5.1%. Humans typically miss one to two words out of every 20 words they hear. In a five-minute conversation, that could be as many as 80 words.

The research project includes applying deep learning technologies and incorporating acoustic models. The speech recognition model used Long Short Term Memory (LSTM) and WaveNet language models with a score fusion of three acoustic models. The acoustic models included a LSTM with multiple feature inputs, another LSTM trained with speaker-adversarial multi-task learning and a third model with a residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. The last model learns from positive examples but also takes advantage of negative examples, so it performs better where similar speech patterns are repeated.

Yoshua Bengio from Montreal Institute for Learning Algorithms (MILA) Lab at University of Montreal commented about the speech recognition.

In spite of impressive advances in recent years, reaching human-level performance in AI tasks such as speech recognition or object recognition remains a scientific challenge. Indeed, standard benchmarks do not always reveal the variations and complexities of real data. For example, different data sets can be more or less sensitive to different aspects of the task, and the results depend crucially on how human performance is evaluated, for example using skilled professional transcribers in the case of speech recognition.

He also said IBM research helps with advancing speech recognition by applying neural networks and deep learning into acoustic and language models.

In other speech processing news, IBM added Diarization to their Watson Speech to Text service which helps with use cases like distinguishing individual speakers in a conversation. All these achievements help with introducing technologies that will match the complexity of how the human ear, voice and brain interact.